Master planning typically involves the procurement of raw materials, transforming the raw materials into finished goods and distributing the finished goods to warehouses, retailers and/or customers. This is a typical supply chain where the decisions are more tactical in nature, ranging from procurement, production planning and distribution planning. These tactical decisions are further worked upon to generate operational or execution level decisions, such as, requirements planning, scheduling and transport planning. Many companies are realizing the importance of integration between these two levels, which is critical in industries that have large setup times along with additional constraints of sequence dependency.
Furthermore, the problem is complicated by the fact that often there is a trade-off between inventory and production changeover. For example, producing long runs of a particular product line may decrease changeovers, but it may undesirably increase inventory levels of finished goods or works in progress for a given time frame. In some cases, high inventory is undesirable, for example, because freshness or storage space is a constraint. Further, as one line is produced on a resource for a longer time in order to reduce production changeover, another product, which may have a high demand and low inventory, cannot be simultaneously produced on the same resource. Thus, there are trade-offs between inventory and production changeover that must be taken into account.
The integration of production planning and scheduling in this context mainly arises from the implementability of the plan on the manufacturing floor. Because the setups are sequence-dependent and large, if they are not accounted for during a master plan, the plan becomes infeasible during scheduling. As a result, backlog or shortage is increased. On the other hand, if setups are accounted during master planning, it leads to discrete constraints, changing the problem structure from linear program (LP) to mixed integer program (MIP) and in some cases to mixed integer non linear program (MINLP). Though there are methods for solving each of these on a small scale, when it comes to problems of large scale, there is no method available or acceptable to the customer or planner in terms of plan quality and performance. One of the problems with this is the very large run times (performance) and another is the fundamental understanding of the plan itself.
Therefore, previous techniques of solving campaign planning have proven inadequate and have proven not to be scalable for large scale master planning problems, where tradeoff decisions are made, for at least choosing which plant to manufacture or determining which distribution lane to transport the necessary finished goods.